Metabolic differences and differentially expressed genes between C57BL/6J and C57BL/6N mice substrains

C57BL/6J (B6J) and C57BL/6N (B6N) mice are the most frequently used substrains in C57BL/6 (B6) inbred mice, serving as physiological models for in vivo studies and as background strains to build transgenic mice. However, the differences in metabolic phenotypes between B6J and B6N mice are not coherent, and genotypic differences in metabolically important tissues have not been well studied. The phenotypic differences between B6J and B6N substrains have often been attributed to the role of the nicotinamide nucleotide transhydrogenase (Nnt) gene, whereby B6J has a spontaneous missense mutation of Nnt. Nevertheless, phenotypic differences between the two cannot be explained by Nnt mutations alone, especially in metabolic traits. Therefore, we aimed to investigate the genetic cause of the phenotypic differences between B6J and B6N mice. Determining consistent genetic differences across multiple tissues involved in metabolic traits such as subcutaneous and visceral white adipose tissues, brown adipose tissue, skeletal muscle, liver, hypothalamus, and hippocampus, may help explain phenotypic differences in metabolism between the two substrains. We report candidate genes along with comparative data on body weight, tissue weight, blood components involved in metabolism, and energy balance of B6J and B6N mice. Insulin degrading enzyme, adenylosuccinate synthase 2, and ectonucleotide triphosphate diphosphohydrolase 4 were highly expressed in B6J mice compared with those in B6N mice, and Nnt, WD repeat and FYVE domain containing 1, and dynein light chain Tctex-type 1 were less expressed in B6J mice compared with those in B6N mice in all seven tissues. Considering the extremely wide use of both substrains and their critical importance in generating transgenic and knock-out models, these findings guide future research across several interrelated fields.

Introduction C57BL-derived inbred mouse B6 is currently the most frequently used laboratory animal and is a vital tool in various biomedical studies, with more than 20 different substrains. Among these substrains, B6J and B6N mice are the most commonly used, owing to their strain stability and ease of breeding. B6J was the first mouse substrain that had its genome fully sequenced [1]; thus, many transgenic mice, including those produced using the Cre-lox and FLP-FRT recombination systems, have been generated with a B6J background, and in vivo studies using B6J as physiological or pathological models have increased rapidly. However, the B6N substrain has recently become more common and standardized because it was used as the embryonic stem cell line for large-scale knockout generation and phenotyping projects (e.g., International Knockout Mouse Consortium, International Mouse Phenotyping Consortium, and NIH Knockout Mouse Project), generating more than 5,000 targeted mutant mouse lines that are currently available to researchers worldwide [2]. Thus, both the B6J and B6N mice substrains have become indispensable and exclusive materials. Nevertheless, researchers are indifferent to the genetic and phenotypic differences between the two substrains [3,4], and generate mice with mixed substrains [5], or do not explicitly address the substrain distinction in their publications [6]. Moreover, these unaddressed differences can lead to confounding experimental outcomes or misinterpretations; therefore, comprehensive information about the properties of these mouse substrains needs to be ascertained.
To date, various genomic differences such as indels, structural variations, and single nucleotide polymorphisms (SNPs), as well as phenotypic differences between B6J and B6N, have been reviewed [3,7], some of which are known to be associated with pathogenesis and require caution when conducting experiments in such areas. For instance, ophthalmic problems have been known to occur in B6N mice [7,8], regardless of genetic engineering [9,10], as the substrain carries a single nucleotide deletion that causes a frameshift mutation and subsequent protein truncation in the crumbs cell polarity complex component 1 (Crb1) gene, which is associated with retinal degeneration. Therefore, in ocular research, it would not be appropriate to choose B6N as a background or in vivo model, crossbreed it with B6J, or compare it with B6J. Similarly, these substrain differences must be considered when selecting mouse substrains in metabolic studies.
In this study, we aimed to investigate the genetic causes of the phenotypic differences between B6J and B6N substrains other than Nnt by comparing mice with spontaneous lack of Nnt and Nnt-wild type mice. Metabolic phenotypic differences between B6J and B6N substrains were characterized by examining blood components involved in metabolism, energy balance, and body and tissue weight. Determining consistent genetic differences across multiple tissues involved in metabolic traits such as subcutaneous and visceral white adipose tissues, brown adipose tissue, muscle, liver, and brain may help explain phenotypic differences in metabolism between the two substrains. Considering the extremely wide use of both substrains and their critical importance in generating transgenic and knock-out models, these findings guide future research across several interrelated fields.

Animals and experimental design
All experimental procedures were approved and performed in accordance with the Institutional Animal Care and Use Committee of the RIKEN Yokohama Campus. Seven-week-old male C57BL/6J and C57BL/6NCrl mice were purchased from the Oriental Yeast Company, Ltd. (Shiga, Japan). Genotyping for nicotinamide nucleotide transhydrogenase (Nnt) in both substrains confirmed that only B6J lacks NNT (S1 Fig). Mice were acclimated for 1 week and maintained on an alternating 12 h light/dark cycle at a temperature of 23˚C, with free access to food and water. After the acclimatization period, the mice were randomly divided into two experimental groups per strain (n = 5 per group): normal diet (ND) (CLEA Rodent Diet CE-2; CLEA Japan Inc., Shizuoka, Japan) or a high-fat diet (HF) (High-Fat Diet 32; CLEA Japan Inc.) for 30 weeks. The basic diet composition is shown in S1 Table. At the age of 20-30 weeks old, a metabolic assessment was performed. At the age of 38 weeks old, mice were euthanized under isoflurane anesthesia, and blood and organs were collected. Inguinal white adipose tissue (iWAT), epididymal white adipose tissue (eWAT), brown adipose tissue (BAT), skeletal soleus muscle (muscle), liver, hypothalamus (Hyt), and hippocampus (Hic) were rapidly removed, weighed, and submerged in RNAlater solution (Thermo Fisher Scientific, Waltham, MA, USA) at 4˚C for 20 h, and stored at -20˚C. The timeline of the experiment is demonstrated in S2 Fig.

Metabolic assessments
Oxygen consumption (VO 2 ) and carbon dioxide exhalation (VCO 2 ) were measured using an open-circuit metabolic gas analysis system connected directly to a mass spectrometer (ARCO-2000; Arco Systems Inc., Chiba, Japan). The mice were housed in individual acrylic chambers with free access to food and water. After five days of acclimation, in which food intake and VCO 2 /VO 2 values stabilized, data were recorded in individual mice for 1 min at 15 min intervals over a 7-day period, and mean values for every 12-h were used for the analysis. The measurement protocol including the airflow (0.3 L/min) and data acquisition interval was determined to measure 10 mouse chambers simultaneously along with the calibration gas line Total energy expenditure was calculated based on Lusk's equation [34]. Carbohydrate and fat consumption were calculated based on Frayn's equation [35].  −1 ]. Data were normalized to body weight. Locomotor activity was estimated based on the number of infrared beams broken in both the x-and y-directions using an activity monitoring system combined with a food intake recording system (ACTIMO-100M/MFD-100M; Shin Factory, Fukuoka, Japan).

RNA sequencing
Tissue preparation and RNA isolation. Minced tissues were homogenized with Sepasol RNAI solution (Nacalai Tesque, Kyoto, Japan) using a TissueLyser LT instrument (Qiagen, Hilden, Germany) set at 50 strokes/s for 5 min. The homogenate from adipose tissues was centrifuged at 3000 × g for 10 min, and the bottom layer was transferred into a new tube to separate the fat from the upper layer. Chloroform was then added to the sample, and the vortexed sample was centrifuged at 14000 × g for 10 min to separate the RNA phase. The RNA phase was then transferred to a new tube and subjected to total RNA purification using QIAcube and the RNeasy kit (Qiagen). Quality analysis of RNA samples was performed using TapeStation (Agilent Technologies, Santa Clara, CA, USA) and RNA ScreenTape (Agilent Technologies).
Library construction and sequencing. Libraries were generated with the NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA). mRNA was enriched from total RNA (250 ng) using magnetic poly-T beads. First-and second-strand cDNAs were synthesized using random hexamer primers, M-MuLV reverse transcriptase, DNA polymerase I, and RNase H, followed by the conversion of overhangs to blunt ends. DNA fragments were ligated with NEBNext adaptors and size-fractionated with the AMPure XP system (Beckman Coulter, Inc., CA, USA) before treatment with the USER enzyme (New England Biolabs) and polymerase chain reaction (PCR) amplification with universal and index primers using Phusion high-fidelity DNA polymerase. PCR products were purified using the AMPure XP system, and the quality of the library was assessed using the TapeStation system (Agilent Technologies). Pooled libraries were sequenced on an Illumina HiSeq 2500 platform to obtain 50 bp single-end reads.
Read mapping and quantification of gene expression level. Reads were mapped to genes in the reference mouse genome (UCSC mm9) and assembled into transcripts, whose abundance was estimated as the expected number of fragments per kilobase per million base pairs sequenced (FPKM) using Cufflinks (v 1.3.0). Bowtie (v 0.12.7) was used to build an index of the reference genome, and TopHat (v 1.4.0) was used to align the reads.
Differential gene expression analysis. Data were analyzed using Strand NGS (v. 2.7, Strand Life Sciences, Bengaluru, India). DESeq was used to compare pairs of sample groups that included five biological replicates for each group. All genes were tested to obtain the corresponding p-values, followed by multiple testing corrections using the Benjamini and Hochberg method to acquire the corrected p-value (q). Comparing the significance of differences in gene expression levels between groups was analyzed using Tukey's post hoc test on normalized FPKM.

Statistical analysis
Statistical analyses were performed using GraphPad Prism 8 software. Quantitative two-group data were analyzed using an unpaired two-tailed t-test. A comparison of data with two and three factors was performed using an analysis of variance ANOVA.

B6J mice exhibit lower adiposity than B6N mice
Substrain differences in tissue weight were found in white adipose tissues, muscle, and liver of the ND-fed group. The weights of the white adipose tissues (iWAT and eWAT) of ND-fed B6J mice were significantly (p<0.0001 for both iWAT and eWAT) less than those of B6N mice, when measured in absolute weight (S3A and S3B Fig) and when normalized to body weight (Fig 2A and 2B). Liver and muscle weights were slightly less in absolute weight in ND-fed B6J mice (S3D and S3E Fig); however, liver and muscle weights normalized to body weight were slightly higher in B6J mice (Fig 2D and 2E) compared with those in B6N mice. In the group of mice fed HF, the iWAT weight of B6J mice tended to be lower than that of B6N mice ( The relative weight of BAT of HF-fed B6J mice tended to be higher than that of B6N mice (Fig 2C). Similarly, B6J mice fed HF tended to have higher relative weight of liver and muscle than B6N mice (Fig 2E and 2D). Looking at the effect of the HF diet on tissue weight, HF caused a significant (p = 0.011 for B6J and p = 0.003 for B6N) reduction in the muscle in both B6J and B6N mice ( Fig 2D). In contrast,

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Phenotypic and genetic differences between C57BL/6J and C57BL/6N mice substrains HF significantly (p = 0.002 for B6J and p = 0.013 for B6N) increased liver in both B6J and B6N mice ( Fig 2E). HF also significantly (p = 0.0003 for iWAT and p = 0.025 for BAT) increased iWAT and BAT weights, but only in B6J mice, not in B6N mice (Fig 2A and 2C). Surprisingly, HF did not increase eWAT weight in both B6J and B6N mice; rather, eWAT weight in B6N mice was significantly (p<0.0001) less in the HF group than in the ND group (Fig 2B). Collectively, these results indicate that B6J mice have lower adiposity and more pronounced HFinduced increases in iWAT and BAT than B6N mice. Moreover, the increased fat retention in BAT in B6J mice suggests that metabolic strategies such as burning more energy or increasing the conversion of WAT to BAT may have resulted in less weight gain in B6J mice.

Plasma leptin and adiponectin levels in B6J mice are lower than those in B6N mice
To confirm the lower adiposity in B6J mice than in B6N mice (Fig 2A and S3A Fig), we measured two adipokines: leptin and adiponectin. In the ND-fed groups, the plasma leptin levels of B6J mice were significantly (p = 0.0002) lower than those of B6N mice (Fig 3A). The adiponectin levels were also lower in ND-fed B6J mice than in ND-fed B6N mice but in a slightly weaker trend (p = 0.07, Fig 3B). In the HF-fed groups, adipokine levels were also lower in B6J mice than in B6N mice; however, the difference was not statistically significant (Fig 3A and  3B). Considering that leptin increases with fat accumulation and adiponectin decreases, the leptin/adiponectin ratio correlates well with adiposity [36]. In addition, this ratio is correlated with insulin resistance, which may represent adipose tissue dysfunction [36]. Therefore, we calculated the ratio of leptin to adiponectin and found it to be approximately four-fold lower (p = 0.0002) in B6J mice than in B6N fed ND (Fig 3C), confirming that the B6J substrain has lower adiposity than B6N substrain.

Plasma insulin levels of B6J mice are lower than those of B6N mice
We further compared commonly measured blood metabolic parameters, such as glycemic (glucose and insulin), lipidic (T-Cho, HDL, FFA, and TG), and liver enzymes (ALT, AST, and

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Phenotypic and genetic differences between C57BL/6J and C57BL/6N mice substrains LDH). The only blood components that differed between B6J and B6N mice were insulin and FFA (Fig 4A and 4B). Insulin concentrations were significantly (p = 0.015) lower in B6J mice than in B6N mice under the ND fed condition (Fig 4A). FFA was significantly (p = 0.010) lower in HF-fed B6J mice than in HF-fed B6N mice (Fig 4B). T-Cho (Fig 4C), HDL (Fig 4D), ALT (Fig 4E), AST (Fig 4F), and LDH levels ( Fig 4G) were not statistically different between B6J and B6N mice, in either diet group, whereas these levels were considerably higher in the HF groups than in the ND groups (Fig 4C-4G). Additionally, the blood glucose level in B6J mice and, not in B6N mice was higher in the HF group than in the ND group (Fig 4H). Similarly, TG concentration in B6J mice fed the HF diet were significantly (p = 0.003) lower than in mice fed the ND diet, with less reduction in B6N mice (Fig 4I).

B6J mice consume more energy than B6N mice
To determine the difference in metabolic rates between B6J and B6N mice, we measured oxygen consumption and carbon dioxide production and calculated energy, carbohydrate, and fat consumptions (normalized to body weight). Values are presented per diet and per light/dark phase ("Dark" for the active phase and "Light" for the inactive phase, "24 h" throughout both phases). Although the overall differences between B6J and B6N mice were not large, some marked differences were observed such as energy expenditure, with B6J mice tending to expend more energy than B6N mice during the active phase (Fig 5A). Similarly, there was a notable difference in carbohydrate consumption when fed ND, with B6J mice consuming more carbohydrates than B6N mice during the active phase (Fig 5B). Fat consumption data also showed differences between B6J and B6N when fed HF, with B6J mice consuming more fat than B6N mice throughout the day (Fig 5C). In addition, behavioral factors that affect metabolic rates, such as physical activity and food intake, were assessed. B6J mice tended to be

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Phenotypic and genetic differences between C57BL/6J and C57BL/6N mice substrains more active than B6N mice when fed both ND or HF ( Fig 5D) and took considerably less food ( Fig 5E). Table 1 shows the number of DEGs (p<0.05) in the iWAT, eWAT, BAT, muscle, liver, hypothalamus, and hippocampus between B6J and B6N mice. Comparable numbers of DEGs were upregulated and downregulated in each tissue in the ND and HF groups, respectively. The gene names of the DEGs in each tissue are listed in S2 Table, along with their average fold change (FC), p-values, and corrected p-value (q). Among the DEGs that were highly expressed in B6J mice compared to B6N mice, three genes: insulin degrading enzyme (Ide), adenylosuccinate synthase 2 (Adss2), and ectonucleotide triphosphate diphosphohydrolase 4 (Entpd4) in the ND group and five genes: Ide, Adss2, Entpd4, B-TFIID TATA-box binding protein associated factor 1 (Btaf1), and transmembrane protein 267 (TMEM267) in the HF group overlapped in all seven tissues examined in the present study. Likewise, among the DEGs that showed lower expression in B6J mice compared with in B6N mice, four genes: Nnt, WD repeat and FYVE domain containing 1 (Wdfy1), dynein light chain Tctex-type 1 (Dynlt1), and RAB4A, member RAS oncogene family (Rab4A) in the ND group and three genes: Nnt, Wdfy1, and Dynlt1 in the HF group were identified. Of these overlapping genes, Wdfy1

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Phenotypic and genetic differences between C57BL/6J and C57BL/6N mice substrains  showed high FC and was significantly (q<0.05, S3 Table) lower in ND-fed B6J mice by approximately 4-to 6-fold compared with in ND-fed B6N mice (Fig 6E). Entpd4 also showed consistent FC in the HF group with approximately 2-to 3-fold higher expression in B6J mice than in B6N (Fig 6C). The effect of a high-fat diet on the expression of these overlapping genes was not observed in most tissues. However, decreased Ide expression and increased Dynlt1 expression in the liver (Fig 6A and 6F) and decreased Entpd4 expression in BAT (Fig 6C) were observed under HF conditions compared to those observed under ND conditions.

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Phenotypic and genetic differences between C57BL/6J and C57BL/6N mice substrains due to a spontaneous missense mutation of Nnt in B6J mice is considered to be responsible for these phenotypic differences and abnormal traits in B6J mice [11][12][13][14][15][16][17][18][19][20][21][22][23][24], including metabolic dysfunctions such as impaired glucose tolerance, diminished insulin secretion, and overweightness. This speculation is based on the fact that the enzymatic function of NNT is to pump protons across the inner mitochondrial membrane [26]. However, the difference in the phenotype between B6J and B6N substrain is not always correlated to the absence or presence of NNT. For example, Nnt-deficient B6J mice (S1 Fig) in the present study were considerably underweight compared with B6N mice (Fig 1), as reported by previous studies [15,[29][30][31][32]. However, there are conflicting data on body weight differences: overweight B6J mice [12-14, 27, 28] or unchanged weight [16,17,33]. Similarly, there are also conflicting data on the differences in blood insulin and glucose levels between B6J and B6N mice with several studies reporting that B6J mice have lower insulin levels [12-14, 17-19, 29, 31], higher glucose levels [12,15,18,19,31], and lower glucose levels [14] compared to B6N mice, whereas some studies did not report any differences in glucose [16,17,29,31,33] or insulin [16,31] levels. In our study, we observed higher glucose levels but lower insulin levels in Nnt-deficient B6J mice compared with Nnt-wild type B6N mice (Fig 4). Moreover, literature on DEGs between B6N, B6J, and B6J expressing the full-length Nnt gene (B6JBAC) in the adrenal, testes, and heart has shown that there were no common DEGs across tissues when comparing the B6J and B6JBAC mice, despite the presence of far more DEGs in both compared with in B6N mice, suggesting a very modest effect of NNT status on transcriptome regulation and phenotype differences [52]. Given the discrepancy between the B6J and B6N phenotypes and NNT levels, we speculate that genetic mutations other than Nnt mutation alleles are present in either B6J or B6N substrains.
We investigated DEGs between B6J and B6N substrains in seven tissues involved in metabolic function: iWAT, eWAT, BAT, muscle, liver, hypothalamus, and hippocampus tissues, assuming that, besides Nnt, there are common genes across tissues regulating metabolic phenotypes that may explain the substrain differences. As a result, six DEGs, including Nnt, overlapped in all seven tissues regardless of dietary conditions (Fig 6). Three DEGs: Ide, Adss2, and Entpd4 were significantly (p<0.05) more expressed in B6J mice than in B6N mice, whereas three DEGs: Nnt, Wdfy1, and Dynlt1 were significantly (p<0.05) less expressed in B6J mice compared with B6N mice.
Previously, Wdfy1 and Entpd4 have been reported to be differentially expressed in the brain and pancreas between B6J and B6N [39,53]; thus, our findings are consistent with those of this study. High Wdfy1 expression in the brain of B6N is speculated to be related to reduced alcohol intake in B6N mice [39], whereas low Wdfy1 expression in the pancreas of B6J mice is associated with the progression of chronic pancreatitis in B6J mice [53]. Although these previous studies did not focus on Entpd4 [39,53], its higher expression in B6J mice compared with that in B6N mice is consistent with the finding of the present study.
Watkins-Chow and Pavan have reported that the presence of increased copy number variation (CNV) at the Ide locus in B6J mice results in increased Ide expression [54]. The CNV may contribute to the divergence in Ide expression between B6J and B6N observed in this study. The lists of B6N substrains of protein-inactivating sequence variations (sequence variations causing premature stop codons, loss of stop codons and SNPs, and short in-frame insertions and deletions) that referenced the B6J mouse genome [55] did not include the Ide, Adss2, Entpd4, Wdfy1, and Dynlt1 genes identified in this study. Therefore, CNVs in B6J and B6N mice should be investigated in addition to indels and SNPs [56]. Additionally, IDE is an enzyme that degrades insulin. Considering the lower blood insulin concentration and slightly higher glucose concentration in B6J mice compared with those in B6N mice (Fig 4A  and 4H), the higher Ide expression level in B6J mice is consistent with the lower blood insulin concentration (Fig 6A). These results suggest that Ide may be responsible for the phenotypic differences in insulin secretion between B6J and B6N mice. Ide also plays a role in type 2 diabetes and Alzheimer's disease [57]; thus, caution should be exercised when using B6 substrains in studies targeting these diseases.
A previous study has reported that mice lacking nucleobindin 2 (NUCB2), a precursor of nesfatin involved in appetite regulation, exhibit insulin resistance and high Wdfy1 expression in their visceral macrophages [58], suggesting that Wdfy1 may be also involved in insulin signaling. However, since the genetic background of the floxed mice used in the NUCB2 paper [58] was B6J, and that of the crossbred recombinase mice was "C57BL/6J; C57BL/6N" [59], we cannot rule out the possibility that high Wdfy1 expression was simply due to comparing B6N, which has high Wdfy1 expression, with B6J, which has low expression. Similarly, the use of mice with a mixed background of B6J and B6N in immunological studies leads to confounding result interpretations. For example, the effect of granzyme A (GZMA) on viral arthritis remains inconclusive because the phenotypes observed in experiments using GZMA knockout mice vary from paper to paper [60]. However, the genetic background of GZMA knockout mice was found to be a mix of B6J and B6N, indicating that viral arthritis ameliorated in GZMA knockout mice was not the consequence of loss of GZMA expression, but rather of the genetic background of B6N, including Nnt [60].
To the best of our knowledge, no literature on Adss2 and Dynlt1 as DEGs between B6J and B6N mice currently exists. Nevertheless, we have identified DEGs between B6J and B6N (S2 Table), which may help plan mouse experiments. One limitation of this paper is that it lacks the potential to generalize the substrain differences due to the small sample size and the fact that only male mice were selected. Furthermore, gene expression analysis was conducted using an old version of the mouse genome mm9. However, in the DEG analysis between the two substrains, the well-known Nnt was also retrieved as a persistent gene across tissues, and q values for the six genes found here were below 0.1 in most of the conditions: seven tissues and two diets (S3 Table). This suggests that the genotypic data in this study are likely to be applicable and reliable. Our findings may be useful for revisiting past studies that have used B6J, B6N, or B6 substrains to determine whether their results answer the original research objectives or are merely a measurement of the differences between B6J and B6N.

Conclusion
Nnt mutations in B6J mice have been implicated as a cause of obesity and various metabolic abnormalities and are described as the causative genetic explanation for the phenotypic differences between B6J and B6N mice [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. However, there is a discrepancy in metabolic traits between B6J and B6N that cannot be explained by Nnt alone, raising the possibility that mutant alleles other than Nnt exist. We have identified persistent differentially expressed genes between B6J and B6N mice across all seven regions and two diets. Because these genes are associated with several diseases, including type 2 diabetes and Alzheimer's, perhaps B6J and B6N substrains are unsuitable models for these diseases. Considering the extremely wide use of both substrains and their critical importance in generating transgenic and knock-out models, our findings help guide future research across several interrelated fields.
Supporting information S1 Table. Basic diet composition of the normal diet (ND) and high-fat diet (HF). (TIF) S2 Table. Differentially expressed genes between B6J and B6N mice fed a normal diet or a high-fat diet. P-values were calculated using the moderated t-test, followed by multiple testing corrections using the Benjamini-Hochberg method to acquire the corrected p-value (q). Fold change (FC) values were calculated as log2 of B6J relative to B6N. (XLSX) S3 Table. Differentially expressed genes between B6J and B6N mice overlapped across the tissues. P-values were calculated using the moderated t-test, followed by multiple testing corrections using the Benjamini-Hochberg method to acquire the corrected p-value (q). Fold change (FC) values were B6J relative to B6N. ND, normal diet; HF, high-fat diet; iWAT, inguinal white adipose tissue; eWAT, epididymal white adipose tissue; Bat, brown adipose tissue; Muscle, skeletal muscle; Hyt, hypothalamus; Hic, hippocampus.